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Interpretable collaborative data analysis on distributed data
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DOI：10.1016/j.eswa.2021.114891.png)
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摘要
En 中文
This paper proposes an interpretable non-model sharing collaborative data analysis method as a federated learning system, which is an emerging technology for analyzing distributed data. Analyzing distributed data is essential in many applications, such as medicine, finance, and manufacturing, due to privacy and confidentiality concerns. In addition, interpretability of the obtained model plays an important role in the practical applications of federated learning systems. By centralizing intermediate representations, which are individually constructed by each party, the proposed method obtains an interpretable model, achieving collaborative analysis without revealing the individual data and learning models distributed between local parties. Numerical experiments indicate that the proposed method achieves better recognition performance than individual analysis and comparable performance to centralized analysis for both artificial and real-world problems.
Keyword:
Collaborat i v e data analysis
Distributed data
Interpretability
Dimensionality reduction
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